




Master of Eng. in Automation & IT


Automation & IT
Course
Modules
Modelling and Simulation
Modelling and Simulation of Technical Systems
Qualification aims
Students will comprehend methods and technologies of modelling and simulation of continuous and discrete event systems and develop their own small simulators. In particular, they will intensively work with "state of the art“ continuous and discrete event simulation software.
Students can
 model and simulate continuous systems
 model and simulate discrete event systems
 model data based systems (big data)
 model and simulate electrical energy systems
by
 understanding and applying the concepts of modelling of continuous systems, discrete event systems, data based systems, and electrical energy systems
 understanding the concepts of continuous and discrete event simulators
 evaluating advantages and disadvantages of numerical integration methods
 applying modelling and simulation concepts to realworld technical problems
 designing simulation models of technical systems
 evaluating the quality of the simulation models
 performing simulation experiments
 using tools from statistical learning for modeling and understanding complex data sets
 applying standardized processes (e.g., the CRISPDM process) to investigate complex data
 summarizing results in reports
 presenting results in oral presentations
to
 develop a deep understanding of the behavior of technical systems
 analyze and understand data from these systems
 be able to carry out design tasks such as system or plant design, controller design, etc. with the aid of simulation tools
 be qualified for a professional career as automation engineer
Courses
The module consists of four courses:
Modelling and Simulation of Continuous Systems


Tutor 
Prof. Scheuring 
Credit points 
4 CP 
Term 
Spring 
Contents
 Modelling of mechanical systems and processengineering systems (thermodynamics, data on chemical media, valves, pumps, reactors, distillation columns, etc.)
 Design and organization of a simulator
 Sequentialmodular simulation
 Dynamic simulation
 Introduction to UniSim
 Process industry applications
Modelling and Simulation of Discrete Event Systems


Tutor 
Prof. Scheuring 
Credit points 
2 CP 
Term 
Spring 
Contents
 Specifications of discrete event systems
 Compositional modelling of discrete event systems
 Objectoriented simulation of discrete event systems
 Introduction to Plant Simulation (formerly Simple ++, eMPlant)
 Probability distribution
 Queuing theory
 Process and production industry application examples
Datadriven Modelling and Model Optimization


Tutor 
Prof. BartzBeielstein + Prof. Konen 
Credit points 
5 CP 
Term 
Spring 
Contents
 Data from realworld problems (industry, economy, science)
 Data preparation
 Linear regression, logistic regression
 Hypothesis testing
 Classification, linear discriminant analysis
 Treebased methods
 Sequential parameter optimization (SPO)
 Model selection
 Treatment of missing values and huge data sets
 Data visualization
 Data mining, CRISPDM Process
 Learning, especially advanced modelling techniques: Bootstrap, bagging, meta learner (e.g. random forests), empirical learning problems
 Evaluation of modelling results (e.g., error measures, overfitting, cross validation, precision and recall)
Modelling and Simulation of Electrical Energy Systems


Tutor 
Prof. Freiburg 
Credit points 
4 CP 
Term 
Fall 
Contents
 System requirements
 Electrical grids and grid components
 Grid operation
 Transmission line theory
 Stability aspects
 Network planning
 Network simulation
 Distributed energy resources
 Smart grids
 Power quality
Bibliography
 Brenan, K., et.al.: Numerical solution of initial value problems in differential algebraic equations. 99 TLS 1067, ISBN 0444015116
 UniSimDocumentation. Honeywell 2022
 Kelton, W.D., Sadowski, R.P., Sadowski, D.A.: Simulation with Arena. McCrawHill, 2002
 Banks, J.: DiscreteEvent System Simulation, PrenticeHall, 1996
 Liebl: Simulation. 2nd revision, Munich, Oldenbourg, 1995
 Greasley A.: Simulation Modelling for Business. Ashgate Hants 2004
 Feldmann K., Reinhardt G. (Hrsg.): Simulationsbasierte Planungssysteme für Organisation und Produktion. Springer Berlin 1999
 Fishman G.S.: DiscreteEvent Simulation. Springer Series in Operations Research. Springer, 2001
 Kelton, W.D., Sadowski, R.P., Sadowski, D.A.: Simulation with Arena. McGrawHill, 2002
 Witten, I. H., Frank, E.: Data Mining, Hanser, 2nd ed., 2005
 Hastie, T., Tibshirani, R., Friedeman, J.: The Elements of Statistical Learning. Springer, 2001
 James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, 4th edition, 2014
 Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis. McGrawHill, Boston, 2000
 BartzBeielstein, T. et al.: Experimental Methods for the Analysis of Optimization Algorithms. Springer, 2010
 Williams, G.: Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Springer, New York, 2011.
 Papailiou: Handbook of Power Systems. Springer, CIGRE
 GomezExposito, A.; Conejo, A.; Canizares, C.: Electric Energy Systems Analysis and Operation. CRC Press
 Elgerd, O.: Electric Energy Systems Theory An Introduction. McGraw Hill
 Mc Donald, J.: Electric Power Substations Engineering. CRC Press
 ABB: Distribution Automation Handbook Elements of power distribution systems. Online available
 Schwab, A.J.: Elektroenergiesysteme. Springer Vieweg



